First of all, learning methods can leverage highly expressive deep neural network as an universal approximator, and automatically discover features relevant to task. This property enables learned models to be resilience to circumstances, such as featureless areas, dynamic lightning conditions, motion blur, accurate camera calibration, which are challenging to model by hand [1]}. As a representative example, visual odometry has achieved notable improvements in terms of robustness by incorporating data-driven methods in its design [2]}, [3]}, outperforming the state-of-the-art conventional algorithms. Moreover, learning approaches are able to connect abstract elements with human understandable terms[4]}, [5]}, such as semantics labelling in SLAM, which is hard to describe in a formal mathematical way.
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